一个多模型机器学习框架,用于识别Raloxifene作为FDA批准的药物中的新型RNA聚合酶抑制剂
Nhung Thi Hong Van1, Minh Tuan Nguyen2
1Department of Physiology, Dongguk University College of Medicine, Gyeongju 38066, Republic of Korea.
Current issues in molecular biology
|July 23, 2025
概括
这项研究使用机器学习来发现新的抗病毒药物,针对RNA依赖RNA聚合酶 (RdRP). 拉洛西芬成为一个有前途的候选人,显示了广泛抗病毒活性的潜力.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 依赖RNA的RNA聚合酶 (RdRP) 是抗病毒药物开发的关键目标.
- 识别新型RdRP抑制剂对于对抗病毒感染至关重要.
研究的目的:
- 开发和验证用于识别RdRP抑制剂的机器学习框架.
- 选FDA批准的药物,以检测潜在的RdRP抑制活性.
- 用计算方法评估拉洛西芬作为潜在的RdRP抑制剂.
主要方法:
- 集成机器学习模型 (ExtraTreesClassifier,RandomForestClassifier,LGBMClassifier,BernoulliNB,BaggingClassifier) 与一个卷积神经网络 (CNN) 结合在一起.
- 使用PubChem数据集AID 588519进行培训和验证.
- 在诺罗病毒RdRP (PDB:4NRT) 上进行了分子对接和分子动力学模拟.
主要成果:
- 组合模型实现了精度,ROC-AUC和F1得分>0.70.
- 在外部验证中,CNN模型的特异性为0.77.
- 拉洛西芬表现出有利的结合亲和力 (-8.8 kcal/mol) 和稳定的结合动态与诺罗病毒RdRP.
结论:
- 开发的机器学习框架有效地识别了潜在的RdRP抑制剂.
- 拉洛西芬是RdRP抑制的有希望的候选者,需要实验验证.
- 这些发现支持拉洛西芬作为广泛的抗病毒药物的潜力.
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